Can structural correspondences ground real world representational content in Large Language Models?

Basic large language models (LLMs) have no direct contact with extra-linguistic reality: Their inputs, outputs and training data consist solely of text. Can they represent the world beyond that text, nevertheless? This paper considers whether LLMs represent real-world domains partly thanks to structural correspondences between their internal states and those domains. I clarify the requirements for a structural correspondence to play a genuinely content-grounding role, and argue (i) that it is a live empirical possibility that text-based LLMs meet those requirements with respect to real-world contents, but (ii) that this requires carefully eliminating alternative, more deflationary, content assignments.